Fire detection devices such as smoke detectors and/or gas detectors are generally employed in structures or machines to monitor the environmental conditions within the living area or occupied compartments of a machine. These devices typically provide an audible or visual warning upon detection of a change in environmental conditions that are generally accepted as a precursor to a fire event or other hazardous condition.
Typically, smoke detectors include a smoke sensing chamber, exposed to the area of interest. The smoke detector's smoke sensing chamber is coupled to an ASIC or a microprocessor circuit. The microprocessor or the ASIC performs the signal processing functions. The smoke sensor samples the qualities of the exposed atmosphere and when a predetermined change in the atmosphere of the exposed chamber is detected by the microprocessor or ASIC, an alarm is sounded.
There are two types of smoke sensors that are in common use: optical or photoelectric type smoke sensors and ionization type smoke sensors. Photoelectric-based detectors are based on sensing light intensity that is scattered from smoke particles. Light from a source (e.g. LED) is scattered and sensed by a photosensor. When the sensor detects a certain level of light intensity, an alarm is triggered.
Ionization-type smoke detectors are typically based on a radioactive material that ionizes some of the molecules in the surrounding gas environment. The current of the ions is measured. If smoke is present, then smoke particles neutralize the ions and the ion current is decreased, triggering an alarm.
The ionization smoke detectors that are currently available in the market are very sensitive to fast flaming fires. This type of fire produces considerable energy and ionized particles, which are easily detected by an ionization sensor.
Smoldering fires most commonly result from cigarette ignition of materials found in homes such as sofas and beds. A smoldering fire typically produces cold smoke particles of which only a small portion is ionized. Because ionization technology focuses on detection of ionized particles, smoldering fire detection with an ionization sensor is typically inconsistent.
The recent advent of reliable microprocessors at a relatively low cost has led to the incorporation of microprocessor technology into the hazardous condition detector field. Attempts to achieve consistent and reliable detection of different types of fire events has led designers to combine various sensor technologies, ionization, photoelectric, optical gas or chemical based sensors into a single unit or system, and employ the signal processing abilities of microprocessors to simultaneously monitor the plurality of sensors. However the combination of various types of sensors, having various signal characteristics tends to be computationally intensive and somewhat inefficient.
Such a system is disclosed in U.S. Pat. No. 7,327,247 in which outputs from a plurality of different types of ambient condition sensors are cross-correlated to adjust a threshold value for a different, primary, sensor. The cross-correlation processing can be carried out locally in a detector or remotely. To minimize false alarming, the alarm determination may be skipped if the output from the primary sensor does not exhibit at least a predetermined variation from an average value thereof. This cross-correlation type of processing used in these combination systems can be very computationally inefficient, thus requiring significant computing resources. In addition, these combination type systems are complex and rather expensive, when one considers the expensive involved in using various sensors, and in employing one or more microprocessors having the required computing power resident thereon. Heretofore, this approach is typical of the current solutions for consistent detection of flaming and smoldering fires.
Other approaches to achieve adequate detection of fires with low false alarm rates incorporate various filtering methods, which are typically used to prevent false or nuisance alarms. These conventional methods typically are also rather inefficient in that they either unnecessarily delay the detection of a fire event, or they require unnecessary processing of the signal. This delays fire event detection and significantly increases the system's power consumption. The requirement for more computing power resident on the chip also increases the expense of the microprocessor and/or ASIC, and ultimately the costs of the system.
Such a system is disclosed in U.S. Pat. No. 5,736,928, which is directed to an apparatus and a method to pre-process an output signal from an ambient condition sensor. The preprocessing removes noise pulses which are not correlated with an ambient condition being sensed. The preprocessing is carried out by comparing the present output value to a prior output value and selecting a minimum value there between. The apparatus and methods incorporate storage for two prior values and the present output value is compared to the two prior values. A minimum or a maximum of the three values is selected.
Additional processing is typically carried out by comparing the present output value to a nominal expected clear air output value, and if the present value exceeds the nominal expected output value, a minimum is selected among the present output value and one or more prior values. If the present output value is less than the nominally expected value, a maximum is selected from among the present output value and one or more prior output values. This approach is computationally inefficient in that the filtering methods used unnecessarily remove relevant signal information which can delay the system's response to a fire event.
Other systems employ multiple filtering operations. One such system is disclosed in U.S. Pat. No. 5,612,674, which describes a noise immune detection system having a plurality of detectors that generate respective indicia representative of adjacent ambient conditions. A communications link extends between the detectors. A control element is coupled to the link to receive and process the indicia and to adjust an alarm threshold level in response to noise levels in the system. Respective indicia are filtered twice by the control element. In the presence of noise, as reflected in relative values of the filtered values of the indicia, the threshold value is automatically increased. This approach tends to be inefficient and unnecessarily expends processing resources. This solution is also rather inefficient in that it requires computational intensive multiple filtering iterations applied to a previously filtered signal.
Smoke and gas sensors can be affected by temperature, humidity, and dust particles. One or a combination of these ambient environmental factors can cause a smoke or gas detector to false alarm.
Traditional methods of compensating for ambient environmental factors typically include adjusting the output of the sensors. Such an approach is disclosed in U.S. Pat. No. 5,798,701, which is directed to a self-adjusting, self-diagnostic smoke detector. The detector includes a microprocessor-based alarm control circuit that periodically checks the sensitivity of a smoke sensing element to a smoke level in a spatial region. The alarm control circuit and the smoke sensor are mounted in a discrete housing that operatively couples the smoke sensor to the region. The microprocessor implements a routine stored in memory by periodically determining a floating adjustment that is used to adjust the output of the smoke sensing element and of any sensor electronics to produce an adjusted output for comparison with an alarm threshold. The floating adjustment is not greater than a maximum value or less than a minimum value. Except at power-up or reset, each floating adjustment is within a predetermined slew limit of the immediately preceding floating adjustment. The floating adjustment is updated with the use of averages of selected signal samples taken during data gathering time intervals having a data gathering duration that is long in comparison to the smoldering time of a slow fire. The adjusted output is used for self-diagnosis.
These self adjusting systems are not optimized for the detection of traditional fires as well as smoldering fire events with a single sensor, nor do they employ multiple fire event specific thresholds from which the processor may select. In addition, the prior art systems tend to employ solutions that are computationally inefficient, expending more of the systems signal processing resources thus requiring the use of more powerful and expensive microprocessors to maintain the same level of flexibility for a system designer.
Thus there exists a need for a computationally efficient method to achieve consistent detection of fast flaming fires as well as smoldering fires using a single ionization type smoke sensor. A system and method that employs an algorithm that is optimized to lower the demands on the computing power resident on the microprocessor employed in a system using less expensive sensors and lower power microprocessors is needed.